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This repository has been archived by the owner on Oct 31, 2023. It is now read-only.
Hello, thanks for your sharing,
I was littile confused about your 1% In1k semi-sup evaluation. You said in paper that the results come from logistic regression on the extracted representations. However, with the same ViT, I found this evaluation of iBoT come from end2end full fintuning(see here), and SwAV et. all fintuned the entire res50 encoder.
The text was updated successfully, but these errors were encountered:
Thanks for your message. Yes, one common evaluation is end-to-end fine-tuning with 100% labels. However, with 1% labels, iBOT achieves the best performance with logistic regression on the extracted (frozen) representations.
Hello, thanks for your sharing,
I was littile confused about your 1% In1k semi-sup evaluation. You said in paper that the results come from logistic regression on the extracted representations. However, with the same ViT, I found this evaluation of iBoT come from end2end full fintuning(see here), and SwAV et. all fintuned the entire res50 encoder.
The text was updated successfully, but these errors were encountered: